A motion detection model inspired by hippocampal function and its applications to obstacle detection

Abstract

We have proposed a motion detection model, CA3–GU–CA1 (CGC) model, inspired by hippocampal function. The CGC model treats edges extracted from monocular image sequences, and detects motion of the edges on segmented 2D maps without image matching. In this paper, we propose an FPGA implementation of the CGC model, in order to achieve low power processing toward practical use. Then, we propose an obstacle detection algorithm using time-to-collision (TTC) based edge grouping. We have evaluated the performance of motion and obstacle detection by using artificial and real image sequences. The results show that the CGC model can achieve high detection rate in complicated situations, and can achieve accurate detection when using a high frame-rate. The proposed obstacle-detection algorithm can detect dangerous objects moving across based on a novel TTC estimation algorithm. Both motion detection and obstacle detection parts can operate at more than 1000fps. The CGC model can also operate with a power dissipation of about 1.4W based on the FPGA implementation.